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Methods to Address Extreme Class Imbalance in Machine Learning Based Network Intrusion Detection Systems.

機(jī)譯：解決基于機(jī)器學(xué)習(xí)的網(wǎng)絡(luò)入侵檢測(cè)系統(tǒng)中極端類不平衡的方法。

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作者
Walter, R. W.;
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年度 2016
頁碼 1-118
總頁數(shù) 118
原文格式 PDF
正文語種 eng
中圖分類
關(guān)鍵詞
Machine learning; Computational science; Computer network security; Network protocols; Intrusion detection; Artificial intelligence; Bayesian networks; Computer languages; Cyberattacks; Information systems; Data mining; Network intrusion detection systems;

機(jī)譯：機(jī)器學(xué)習(xí);計(jì)算科學(xué);計(jì)算機(jī)網(wǎng)絡(luò)安全;網(wǎng)絡(luò)協(xié)議;入侵檢測(cè);人工智能;貝葉斯網(wǎng)絡(luò);計(jì)算機(jī)語言;網(wǎng)絡(luò)攻擊;信息系統(tǒng);數(shù)據(jù)挖掘;網(wǎng)絡(luò)入侵檢測(cè)系統(tǒng);

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專利

1. Addressing Imbalanced Classes Problem of Intrusion Detection System Using Weighted Extreme Learning Machine [J] . Mohammed Awad, Alaeddin Alabdallah International Journal of Computer Networks & Communications . 2019,第5期

機(jī)譯：使用加權(quán)極限學(xué)習(xí)機(jī)解決入侵檢測(cè)系統(tǒng)的不平衡類問題
2. An efficient cascaded method for network intrusion detection based on extreme learning machines [J] . Yu Yuanlong, Ye Zhifan, Zheng Xianghan, Journal of supercomputing . 2018,第11期

機(jī)譯：一種基于極限學(xué)習(xí)機(jī)的高效的網(wǎng)絡(luò)入侵檢測(cè)級(jí)聯(lián)方法
3. Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning [J] . Lan Liu, Pengcheng Wang, Jun Lin, Quality Control, Transactions . 2021,第1期

機(jī)譯：基于機(jī)器學(xué)習(xí)和深度學(xué)習(xí)的基于機(jī)器學(xué)習(xí)的IMAbalance的入侵檢測(cè)
4. Network Intrusion Detection Method Based on Stacked Denoising Sparse Autoencoder and Extreme Learning Machine [C] . Guoling Zhang, Xiaodan Wang, Rui Li, International Conference on Information Technology and Computer Application . 2020

機(jī)譯：基于堆疊去噪稀疏自動(dòng)化器和極限學(xué)習(xí)機(jī)的網(wǎng)絡(luò)入侵檢測(cè)方法
5. Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems [D] . Stefanova, Zheni Svetoslavova. 2018

機(jī)譯：用于網(wǎng)絡(luò)入侵檢測(cè)和入侵防御系統(tǒng)的機(jī)器學(xué)習(xí)方法
6. Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection [O] . Jae-Hyun Seo, Yong-Hyuk Kim 2018

機(jī)譯：機(jī)器學(xué)習(xí)方法用于在類別不平衡數(shù)據(jù)集中優(yōu)化SMOTE比率以進(jìn)行入侵檢測(cè)
7. Machine learning for network based intrusion detection : an investigation into discrepancies in findings with the KDD cup '99 data set and multi-objective evolution of neural network classifier ensembles from imbalanced data [O] . Engen Vegard 2010

機(jī)譯：基于網(wǎng)絡(luò)的入侵檢測(cè)的機(jī)器學(xué)習(xí)：使用KDD杯'99數(shù)據(jù)集和不平衡數(shù)據(jù)的神經(jīng)網(wǎng)絡(luò)分類器集合的多目標(biāo)進(jìn)化研究結(jié)果的差異

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Methods to Address Extreme Class Imbalance in Machine Learning Based Network Intrusion Detection Systems.

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